# Loughney et al., "Tectonic influence on Cenozoic mammal richness and sedimentation history of the Basin and Range, western North America"
# Science Advances
# Correlation analyses and output - first differences

# load files
macrostrat <- read.csv("macrostrat_June-2021.csv", header = TRUE, stringsAsFactors = FALSE)
macrostrat_nonfossil <- read.csv("macrostrat_nonfossil_June-2021.csv", header = TRUE, stringsAsFactors = FALSE)
fossils <- read.csv("fossiliferous_units_January-2021.csv", header = TRUE, stringsAsFactors = FALSE)
deformation <- read.csv("deformation_rates_January-2021.csv", header = TRUE, stringsAsFactors = FALSE)
area <- read.csv("area_change.csv", header = TRUE, stringsAsFactors = FALSE)

# calculate SAR of fossiliferous units of the Basin and Range
brfossilsar <- fossils$NB_SAR + fossils$CB_SAR + fossils$SB_SAR

# correlation analyses
# ----------------------------------------------------------------------------------------------------------------------
# BR
# species richness - deformation rates to 0 Ma
> cor.test(diff(brAllRichness[,2]), diff(deformation$BR_rate))

	Pearsons product-moment correlation

data:  diff(brAllRichness[, 2]) and diff(deformation$BR_rate)
t = -0.91796, df = 69, p-value = 0.3618
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3345702  0.1267107
sample estimates:
       cor 
-0.1098402 

# species richness - deformation rates to 0.5 Ma
> cor.test(diff(brAllRichness[2:72,2]), diff(deformation$BR_rate[2:72]))

	Pearsons product-moment correlation

data:  diff(brAllRichness[2:72, 2]) and diff(deformation$BR_rate[2:72])
t = -1.6193, df = 68, p-value = 0.11
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.40913594  0.04429341
sample estimates:
      cor 
-0.192686 

# species richness - area-change rates to 0 Ma
> cor.test(diff(brAllRichness[,2]), diff(area$BR_rate_change_sum))

	Pearsons product-moment correlation

data:  diff(brAllRichness[, 2]) and diff(area$BR_rate_change_sum)
t = 1.4242, df = 69, p-value = 0.1589
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.0669587  0.3870300
sample estimates:
      cor 
0.1689849 

# species richness - area-change rates to 0.5 Ma
> cor.test(diff(brAllRichness[2:72,2]), diff(area$BR_rate_change_sum[2:72]))

	Pearsons product-moment correlation

data:  diff(brAllRichness[2:72, 2]) and diff(area$BR_rate_change_sum[2:72])
t = 3.4651, df = 68, p-value = 0.0009224
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.1676809 0.5704400
sample estimates:
      cor 
0.3873914 

# species richness - macrostrat SAR to 0 Ma
> cor.test(diff(brAllRichness[,2]), diff(macrostrat$BR_SAR))

	Pearsons product-moment correlation

data:  diff(brAllRichness[, 2]) and diff(macrostrat$BR_SAR)
t = 14.878, df = 69, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.8034973 0.9191878
sample estimates:
      cor 
0.8731258 

# species richness - macrostrat SAR to 0.5 Ma
> cor.test(diff(brAllRichness[2:72,2]), diff(macrostrat$BR_SAR[2:72]))

	Pearsons product-moment correlation

data:  diff(brAllRichness[2:72, 2]) and diff(macrostrat$BR_SAR[2:72])
t = -0.0022993, df = 68, p-value = 0.9982
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2352374  0.2347106
sample estimates:
          cor 
-0.0002788284 

# species richness - number of macrostrat packages to 0 Ma
> cor.test(diff(brAllRichness[,2]), diff(macrostrat$BR_number))

	Pearsons product-moment correlation

data:  diff(brAllRichness[, 2]) and diff(macrostrat$BR_number)
t = 2.9381, df = 69, p-value = 0.004486
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.1086110 0.5258577
sample estimates:
     cor 
0.333465 

# species richness - number of macrostrat packages to 0.5 Ma
> cor.test(diff(brAllRichness[2:72,2]), diff(macrostrat$BR_number[2:72]))

	Pearsons product-moment correlation

data:  diff(brAllRichness[2:72, 2]) and diff(macrostrat$BR_number[2:72])
t = -0.68287, df = 68, p-value = 0.497
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3114617  0.1554612
sample estimates:
        cor 
-0.08252751 

# species richness - fossiliferous unit SAR to 0 Ma
> cor.test(diff(brAllRichness[,2]), diff(brfossilsar))

	Pearsons product-moment correlation

data:  diff(brAllRichness[, 2]) and diff(brfossilsar)
t = 4.5276, df = 69, p-value = 2.426e-05
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.2761144 0.6403885
sample estimates:
      cor 
0.4785882 

# species richness - fossiliferous unit SAR to 0.5 Ma
> cor.test(diff(brAllRichness[2:72,2]), diff(brfossilsar[2:72]))

	Pearsons product-moment correlation

data:  diff(brAllRichness[2:72, 2]) and diff(brfossilsar[2:72])
t = 1.4584, df = 68, p-value = 0.1493
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.06341463  0.39304438
sample estimates:
      cor 
0.1741544 

# species richness - number of fossiliferous units to 0 Ma
> cor.test(diff(brAllRichness[,2]), diff(fossils$BR_number))

	Pearsons product-moment correlation

data:  diff(brAllRichness[, 2]) and diff(fossils$BR_number)
t = -0.85968, df = 69, p-value = 0.3929
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3283609  0.1335682
sample estimates:
       cor 
-0.1029434 

# species richness - number of fossiliferous units to 0.5 Ma
> cor.test(diff(brAllRichness[2:72,2]), diff(fossils$BR_number[2:72]))

	Pearsons product-moment correlation

data:  diff(brAllRichness[2:72, 2]) and diff(fossils$BR_number[2:72])
t = -1.7051, df = 68, p-value = 0.09274
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.41759352  0.03410849
sample estimates:
       cor 
-0.2024884 

# species richness - number of fossil localities to 0 Ma
> cor.test(diff(brAllRichness[,2]), diff(brBinLocs[,2]))

	Pearsons product-moment correlation

data:  diff(brAllRichness[, 2]) and diff(brBinLocs[, 2])
t = 28.698, df = 69, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9373240 0.9753033
sample estimates:
      cor 
0.9605693 

# species richness - number of fossil localities to 0.5 Ma
> cor.test(diff(brAllRichness[2:72,2]), diff(brBinLocs[2:72,2]))

	Pearsons product-moment correlation

data:  diff(brAllRichness[2:72, 2]) and diff(brBinLocs[2:72, 2])
t = 12.554, df = 68, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7476822 0.8950133
sample estimates:
      cor 
0.8358153 

# --------------------------------------------------------------------------------------------------------------
# NB
# species richness - deformation rates to 0 Ma
> cor.test(diff(nbAllRichness[,2]), diff(deformation$NB_rate))

	Pearsons product-moment correlation

data:  diff(nbAllRichness[, 2]) and diff(deformation$NB_rate)
t = 0.27719, df = 69, p-value = 0.7825
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2015200  0.2645967
sample estimates:
       cor 
0.03335179 

# species richness - deformation rates to 0.5 Ma
> cor.test(diff(nbAllRichness[2:72,2]), diff(deformation$NB_rate[2:72]))

	Pearsons product-moment correlation

data:  diff(nbAllRichness[2:72, 2]) and diff(deformation$NB_rate[2:72])
t = 0.59498, df = 68, p-value = 0.5538
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1658135  0.3018344
sample estimates:
       cor 
0.07196435 

# species richness - area-change rates to 0 Ma
> cor.test(diff(nbAllRichness[,2]), diff(area$NB_rate_change_sum))

	Pearsons product-moment correlation

data:  diff(nbAllRichness[, 2]) and diff(area$NB_rate_change_sum)
t = 0.091972, df = 69, p-value = 0.927
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2228078  0.2437456
sample estimates:
       cor 
0.01107144 

# species richness - area-change rates to 0.5 Ma
> cor.test(diff(nbAllRichness[2:72,2]), diff(area$NB_rate_change_sum[2:72]))

	Pearsons product-moment correlation

data:  diff(nbAllRichness[2:72, 2]) and diff(area$NB_rate_change_sum[2:72])
t = 1.4672, df = 68, p-value = 0.1469
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.06237289  0.39392832
sample estimates:
      cor 
0.1751684 

# species richness - macrostrat SAR to 0 Ma
> cor.test(diff(nbAllRichness[,2]), diff(macrostrat$NB_SAR))

	Pearsons product-moment correlation

data:  diff(nbAllRichness[, 2]) and diff(macrostrat$NB_SAR)
t = 12.865, df = 69, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7547283 0.8974995
sample estimates:
      cor 
0.8401055 

# species richness - macrostrat SAR to 0.5 Ma
> cor.test(diff(nbAllRichness[2:72,2]), diff(macrostrat$NB_SAR[2:72]))

	Pearsons product-moment correlation

data:  diff(nbAllRichness[2:72, 2]) and diff(macrostrat$NB_SAR[2:72])
t = 0.93336, df = 68, p-value = 0.3539
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1258314  0.3384965
sample estimates:
     cor 
0.112468 

# species richness - number of macrostrat packages to 0 Ma
> cor.test(diff(nbAllRichness[,2]), diff(macrostrat$NB_number))

	Pearsons product-moment correlation

data:  diff(nbAllRichness[, 2]) and diff(macrostrat$NB_number)
t = 4.0198, df = 69, p-value = 0.0001466
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.2251782 0.6071945
sample estimates:
      cor 
0.4355979 

# species richness - number of macrostrat packages to 0.5 Ma
> cor.test(diff(nbAllRichness[2:72,2]), diff(macrostrat$NB_number[2:72]))

	Pearsons product-moment correlation

data:  diff(nbAllRichness[2:72, 2]) and diff(macrostrat$NB_number[2:72])
t = -0.62726, df = 68, p-value = 0.5326
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3053794  0.1620136
sample estimates:
        cor 
-0.07584792 

# species richness - fossiliferous unit SAR to 0 Ma
> cor.test(diff(nbAllRichness[,2]), diff(fossils$NB_SAR))

	Pearsons product-moment correlation

data:  diff(nbAllRichness[, 2]) and diff(fossils$NB_SAR)
t = 8.0414, df = 69, p-value = 1.679e-11
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.5517777 0.7991641
sample estimates:
     cor 
0.695543 

# species richness - fossiliferous unit SAR to 0.5 Ma
> cor.test(diff(nbAllRichness[2:72,2]), diff(fossils$NB_SAR[2:72]))

	Pearsons product-moment correlation

data:  diff(nbAllRichness[2:72, 2]) and diff(fossils$NB_SAR[2:72])
t = 3.0734, df = 68, p-value = 0.003044
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.1244792 0.5399065
sample estimates:
      cor 
0.3492382 

# species richness - number of fossiliferous units to 0 Ma
> cor.test(diff(nbAllRichness[,2]), diff(fossils$NB_number))

	Pearsons product-moment correlation

data:  diff(nbAllRichness[, 2]) and diff(fossils$NB_number)
t = 2.2654, df = 69, p-value = 0.02663
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.03175924 0.46770681
sample estimates:
      cor 
0.2631134 

# species richness - number of fossiliferous units to 0.5 Ma
> cor.test(diff(nbAllRichness[2:72,2]), diff(fossils$NB_number[2:72]))

	Pearsons product-moment correlation

data:  diff(nbAllRichness[2:72, 2]) and diff(fossils$NB_number[2:72])
t = 0.73249, df = 68, p-value = 0.4664
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1496051  0.3168656
sample estimates:
       cor 
0.08847928 

# species richness - number of fossil localities to 0 Ma
> cor.test(diff(nbAllRichness[,2]), diff(nbBinLocs[,2]))

	Pearsons product-moment correlation

data:  diff(nbAllRichness[, 2]) and diff(nbBinLocs[, 2])
t = 23.64, df = 69, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9105742 0.9644655
sample estimates:
      cor 
0.9434509 

# species richness - number of fossil localities to 0.5 Ma
> cor.test(diff(nbAllRichness[2:72,2]), diff(nbBinLocs[2:72,2]))

	Pearsons product-moment correlation

data:  diff(nbAllRichness[2:72, 2]) and diff(nbBinLocs[2:72, 2])
t = 11.157, df = 68, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7018383 0.8740084
sample estimates:
      cor 
0.8041903 

# -----------------------------------------------------------------------------------------------------------------------
# CB
# species richness - deformation rates to 0 Ma
> cor.test(diff(cbAllRichness[,2]), diff(deformation$CB_rate))

	Pearsons product-moment correlation

data:  diff(cbAllRichness[, 2]) and diff(deformation$CB_rate)
t = 0.34561, df = 69, p-value = 0.7307
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1936114  0.2722335
sample estimates:
       cor 
0.04157004 

# species richness - deformation rates to 0.5 Ma
> cor.test(diff(cbAllRichness[2:72,2]), diff(deformation$CB_rate[2:72]))

	Pearsons product-moment correlation

data:  diff(cbAllRichness[2:72, 2]) and diff(deformation$CB_rate[2:72])
t = 0.92928, df = 68, p-value = 0.356
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1263151  0.3380612
sample estimates:
      cor 
0.1119826 

# species richness - area-change rates to 0 Ma
> cor.test(diff(cbAllRichness[,2]), diff(area$CB_rate_change_sum))

	Pearsons product-moment correlation

data:  diff(cbAllRichness[, 2]) and diff(area$CB_rate_change_sum)
t = -0.58638, df = 69, p-value = 0.5595
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2988116  0.1656075
sample estimates:
        cor 
-0.07041688 

# species richness - area-change rates to 0.5 Ma
> cor.test(diff(cbAllRichness[2:72,2]), diff(area$CB_rate_change_sum[2:72]))

	Pearsons product-moment correlation

data:  diff(cbAllRichness[2:72, 2]) and diff(area$CB_rate_change_sum[2:72])
t = -0.31437, df = 68, p-value = 0.7542
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2706469  0.1986568
sample estimates:
        cor 
-0.03809552 

# species richness - macrostrat SAR to 0 Ma
> cor.test(diff(cbAllRichness[,2]), diff(macrostrat$CB_SAR))

	Pearsons product-moment correlation

data:  diff(cbAllRichness[, 2]) and diff(macrostrat$CB_SAR)
t = 13.923, df = 69, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7821806 0.9097935
sample estimates:
      cor 
0.8587709 

# species richness - macrostrat SAR to 0.5 Ma
> cor.test(diff(cbAllRichness[2:72,2]), diff(macrostrat$CB_SAR[2:72]))

	Pearsons product-moment correlation

data:  diff(cbAllRichness[2:72, 2]) and diff(macrostrat$CB_SAR[2:72])
t = 2.24, df = 68, p-value = 0.02836
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.0289529 0.4682733
sample estimates:
      cor 
0.2621435 

# species richness - number of macrostrat packages to 0 Ma
> cor.test(diff(cbAllRichness[,2]), diff(macrostrat$CB_number))

	Pearsons product-moment correlation

data:  diff(cbAllRichness[, 2]) and diff(macrostrat$CB_number)
t = 1.1519, df = 69, p-value = 0.2533
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.09911657  0.35915726
sample estimates:
      cor 
0.1373636 

# species richness - number of macrostrat packages to 0.5 Ma
> cor.test(diff(cbAllRichness[2:72,2]), diff(macrostrat$CB_number[2:72]))

	Pearsons product-moment correlation

data:  diff(cbAllRichness[2:72, 2]) and diff(macrostrat$CB_number[2:72])
t = 1.4799, df = 68, p-value = 0.1435
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.06085656  0.39521346
sample estimates:
      cor 
0.1766434 

# species richness - fossiliferous unit SAR to 0 Ma
> cor.test(diff(cbAllRichness[,2]), diff(fossils$CB_SAR))

	Pearsons product-moment correlation

data:  diff(cbAllRichness[, 2]) and diff(fossils$CB_SAR)
t = 0.8926, df = 69, p-value = 0.3752
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1296955  0.3318724
sample estimates:
      cor 
0.1068411 

# species richness - fossiliferous unit SAR to 0.5 Ma
> cor.test(diff(cbAllRichness[2:72,2]), diff(fossils$CB_SAR[2:72]))

	Pearsons product-moment correlation

data:  diff(cbAllRichness[2:72, 2]) and diff(fossils$CB_SAR[2:72])
t = 1.2815, df = 68, p-value = 0.2044
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.08446297  0.37500177
sample estimates:
      cor 
0.1535587 

# species richness - number of fossiliferous units to 0 Ma
> cor.test(diff(cbAllRichness[,2]), diff(fossils$CB_number))

	Pearsons product-moment correlation

data:  diff(cbAllRichness[, 2]) and diff(fossils$CB_number)
t = 1.5945, df = 69, p-value = 0.1154
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.04684581  0.40405105
sample estimates:
      cor 
0.1885183 

# species richness - number of fossiliferous units to 0.5 Ma
> cor.test(diff(cbAllRichness[2:72,2]), diff(fossils$CB_number[2:72]))

	Pearsons product-moment correlation

data:  diff(cbAllRichness[2:72, 2]) and diff(fossils$CB_number[2:72])
t = 0.91018, df = 68, p-value = 0.3659
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1285792  0.3360213
sample estimates:
      cor 
0.1097095 

# species richness - number of fossil localities to 0 Ma
> cor.test(diff(cbAllRichness[,2]), diff(cbBinLocs[,2]))

	Pearsons product-moment correlation

data:  diff(cbAllRichness[, 2]) and diff(cbBinLocs[, 2])
t = 13.442, df = 69, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7702531 0.9044794
sample estimates:
      cor 
0.8506861 

# species richness - number of fossil localities to 0.5 Ma
> cor.test(diff(cbAllRichness[2:72,2]), diff(cbBinLocs[2:72,2]))

	Pearsons product-moment correlation

data:  diff(cbAllRichness[2:72, 2]) and diff(cbBinLocs[2:72, 2])
t = 9.1413, df = 68, p-value = 1.877e-13
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.6148227 0.8322872
sample estimates:
      cor 
0.7425259 

# ----------------------------------------------------------------------------------------------------------
# SB
# species richness - deformation rates to 0 Ma
> cor.test(diff(sbAllRichness[,2]), diff(deformation$SB_rate))

	Pearsons product-moment correlation

data:  diff(sbAllRichness[, 2]) and diff(deformation$SB_rate)
t = -1.286, df = 69, p-value = 0.2027
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3729836  0.0832834
sample estimates:
      cor 
-0.152993 

# species richness - deformation rates to 0.5 Ma
> cor.test(diff(sbAllRichness[2:72,2]), diff(deformation$SB_rate[2:72]))

	Pearsons product-moment correlation

data:  diff(sbAllRichness[2:72, 2]) and diff(deformation$SB_rate[2:72])
t = -2.8034, df = 68, p-value = 0.006583
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.51769362 -0.09400992
sample estimates:
       cor 
-0.3218738 

# species richness - area-change rates to 0 Ma
> cor.test(diff(sbAllRichness[,2]), diff(area$SB_rate_change_sum))

	Pearsons product-moment correlation

data:  diff(sbAllRichness[, 2]) and diff(area$SB_rate_change_sum)
t = -0.35217, df = 69, p-value = 0.7258
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2729647  0.1928511
sample estimates:
        cor 
-0.04235848 

# species richness - area-change rates to 0.5 Ma
> cor.test(diff(sbAllRichness[2:72,2]), diff(area$SB_rate_change_sum[2:72]))

	Pearsons product-moment correlation

data:  diff(sbAllRichness[2:72, 2]) and diff(area$SB_rate_change_sum[2:72])
t = -1.5826, df = 68, p-value = 0.1181
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.40549860  0.04864484
sample estimates:
       cor 
-0.1884836 

# species richness - macrostrat SAR to 0 Ma
> cor.test(diff(sbAllRichness[,2]), diff(macrostrat$SB_SAR))

	Pearsons product-moment correlation

data:  diff(sbAllRichness[, 2]) and diff(macrostrat$SB_SAR)
t = 15.278, df = 69, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.8115868 0.9227187
sample estimates:
      cor 
0.8785419 

# species richness - macrostrat SAR to 0.5 Ma
> cor.test(diff(sbAllRichness[2:72,2]), diff(macrostrat$SB_SAR[2:72]))

	Pearsons product-moment correlation

data:  diff(sbAllRichness[2:72, 2]) and diff(macrostrat$SB_SAR[2:72])
t = -0.5621, df = 68, p-value = 0.5759
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2982156  0.1696784
sample estimates:
        cor 
-0.06800707 

# species richness - number of macrostrat packages to 0 Ma
> cor.test(diff(sbAllRichness[,2]), diff(macrostrat$SB_number))

	Pearsons product-moment correlation

data:  diff(sbAllRichness[, 2]) and diff(macrostrat$SB_number)
t = -0.97082, df = 69, p-value = 0.335
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3401739  0.1204838
sample estimates:
       cor 
-0.1160829 

# species richness - number of macrostrat packages to 0.5 Ma
> cor.test(diff(sbAllRichness[2:72,2]), diff(macrostrat$SB_number[2:72]))

	Pearsons product-moment correlation

data:  diff(sbAllRichness[2:72, 2]) and diff(macrostrat$SB_number[2:72])
t = -0.74141, df = 68, p-value = 0.461
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3178342  0.1485519
sample estimates:
        cor 
-0.08954786 

# species richness - fossiliferous unit SAR to 0 Ma
> cor.test(diff(sbAllRichness[,2]), diff(fossils$SB_SAR))

	Pearsons product-moment correlation

data:  diff(sbAllRichness[, 2]) and diff(fossils$SB_SAR)
t = -1.0575, df = 69, p-value = 0.294
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3493015  0.1102630
sample estimates:
       cor 
-0.1262895 

# species richness - fossiliferous unit SAR to 0.5 Ma
> cor.test(diff(sbAllRichness[2:72,2]), diff(fossils$SB_SAR[2:72]))

	Pearsons product-moment correlation

data:  diff(sbAllRichness[2:72, 2]) and diff(fossils$SB_SAR[2:72])
t = -0.35096, df = 68, p-value = 0.7267
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2747506  0.1943945
sample estimates:
       cor 
-0.0425218 

# species richness - number of fossiliferous units to 0 Ma
> cor.test(diff(sbAllRichness[,2]), diff(fossils$SB_number))

	Pearsons product-moment correlation

data:  diff(sbAllRichness[, 2]) and diff(fossils$SB_number)
t = -2.281, df = 69, p-value = 0.02564
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.46912391 -0.03357273
sample estimates:
       cor 
-0.2648024 

# species richness - number of fossiliferous units to 0 Ma
> cor.test(diff(sbAllRichness[2:72,2]), diff(fossils$SB_number[2:72]))

	Pearsons product-moment correlation

data:  diff(sbAllRichness[2:72, 2]) and diff(fossils$SB_number[2:72])
t = 0.023323, df = 68, p-value = 0.9815
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2323001  0.2376444
sample estimates:
        cor 
0.002828285 

# species richness - number of fossil localities to 0 Ma
> cor.test(diff(sbAllRichness[,2]), diff(sbBinLocs[,2]))

	Pearsons product-moment correlation

data:  diff(sbAllRichness[, 2]) and diff(sbBinLocs[, 2])
t = 19.824, df = 69, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.8779096 0.9509808
sample estimates:
      cor 
0.9223068 

# species richness - number of fossil localities to 0.5 Ma
> cor.test(diff(sbAllRichness[2:72,2]), diff(sbBinLocs[2:72,2]))

	Pearsons product-moment correlation

data:  diff(sbAllRichness[2:72, 2]) and diff(sbBinLocs[2:72, 2])
t = 11.461, df = 68, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7126753 0.8790324
sample estimates:
      cor 
0.8117188 